Applied Machine Learning (3 credits)
This class teaches the practical side of machine learning for applications. The emphasis will be on learning the process of applying machine learning effectively to a variety of problems rather than emphasizing the theory behind what makes machine learning work. We will spend time on the practical, hands-on skills for getting machine algorithms to work well on an application. This course does not assume any prior exposure to machine learning theory or practice.
We will cover a wide range of learning algorithms that can be applied to a variety of problems. In particular, we will cover topics such as decision trees, regression, support vector machines, Bayesian networks, Artificial Neural Networks, and clustering. In addition to readings from the course textbook, we will have additional readings from research articles.Note
This course is co-taught with CS438.Pre-requisites